‘Trying to Fly the Plane While We Were Building It’. Applying a Learning Health Systems Approach to Evaluate Early-Stage Barriers and Facilitators to Implementing Primary Care Transformation: a Qualitative Study

ObjectiveA learning health system (LHS) uses data to generate evidence and answer questions required to continually improve system performance and patient care. Given the complexities of practice transformation, an area where LHS is particularly important is the study of primary care transformation (PCT) as PCT generates several practice-level questions that require study where the findings can be readily implemented. In May 2019, a large integrated health delivery system in Minnesota began implementation of a population management PCT in two of its 40 primary care clinics. In this model of care, patients are grouped into one of five service bundles based on their complexity of care; patient appointment lengths and services provided are then tailored to each service bundle. The objective of this study was to examine the use of a LHS in PCT by utilising the Consolidated Framework for Implementation Research (CFIR) to categorise implementation lessons from the initial two PCT clinics to inform further implementation of the PCT within the health system.DesignThis was a formative evaluation in which semistructured qualitative interviews were carried out. Observational field notes were also taken. Inductive coding of the data was performed and resultant codes were mapped to the CFIR.SettingTwo suburban primary care clinics in the Twin Cities, Minnesota.ParticipantsTwenty-two care team members from the first two clinics to adopt the PCT.ResultsSeventeen codes emerged to describe care team members’ perceived implementation influences. Codes occurred in each of the five CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals and process), with most codes occurring in the ‘inner setting’ domain.ConclusionsUsing an LHS approach to determine early-stage implementation influences is key to guiding further PCT implementation, understanding modifications that need to be made and additional research that needs to occur.